System and method for gathering and performing complex analyses on power data from multiple remote sources

ABSTRACT

The invention encompasses data analytics, and more specifically, encompasses the efficient gathering and management of data, and the execution of data analysis solutions on complex power and pricing.

This application is a continuation of U.S. patent application Ser. No.14/071,268, filed Nov. 4, 2013, which is a continuation of U.S. patentapplication Ser. No. 12/399,682, filed on Mar. 6, 2009, which claims thebenefit of priority to U.S. provisional patent application No.61/064,483, filed on Mar. 7, 2008, all of which are incorporated hereinby reference in their entirety.

FIELD OF THE INVENTION

The invention encompasses data analytics, and more specifically,encompasses the efficient gathering and management of data, and theexecution of data analysis solutions on complex power and pricing.

DISCUSSION OF THE RELATED ART

In the power trading markets, power traders demand accurate, powerful,robust, and reliable data analysis systems. Power markets are comprisedof Independent System Operators (ISO) and Regional TransmissionOrganizations (RTO), each of which is responsible for a specificgeographical region that receives power transmission from one or morepower generators. Each ISO/RTO is comprised of one or more regionalpower market, which can be considered as “assigned” to a subsetgeographical power region of its parent set's (i.e., ISO/RTO) region.Moreover, one or more power “nodes” exist within each of the regionalpower markets and, accordingly, belong to that specific regional powermarket. Within each ISO/RTO regional power market, power prices are: (a)established, (b) tracked, and (c) published according to supply anddemand fundamentals, as power is traded and eventually generated. Foreach power node within a specific ISO/RTO regional power market, powerprices vary individually. Therefore, for purposes of the power tradingmarkets, power is typically traded on an hourly basis at each node, in adutch-type auction market. Moreover, there is also a “day ahead” (DA)market that allows traders to bid/offer power into the market on a DAbasis. With respect to the DA market, the ISO/RTO for the specificgeographical region that is affected by one or more given DA tradesdetermines the final DA price for each power node on which a bid/offeris placed. Subsequently, on an hourly basis, each ISO/RTO alerts eachpower trader who placed a bid/offer into its market(s) as to which DAtrades were executed.

As for the operation of the actual power generation markets, whichsignificantly affects the decisions made by power traders as well as thetrading positions they choose to exercise, power generation may becomedisrupted in real time due to multiple factors. Some of these factorsinclude, for example, power congestion, weather-related conditions,unexpected generator/transmission outages, or even differences betweenforecasted and actual power demand (i.e., power “load”). These factorstaken in the aggregate, or individually, can easily disrupt the powergrid. Therefore, as a direct proximate cause, these factors have asignificant impact on power traders' decisions to exercise certain tradeand/or develop certain trading strategies. Furthermore, whether theimpact on the power grid is positive or negative, its root cause(s)simply translates into a proportionally weighed causal factor that“drives one or more power nodes' prices.

Additionally, every power node in an ISO/RTO market is assigned anactual “real time” (RT) price. Similar to the way in which DA trading isperformed, the RT prices are also capable of being traded in a real timemarket (sometimes referred to as a “spot market”), as prices arepublished hourly by the appropriate ISO/RTO. Coupled together, the DAand RT prices are commonly known as Locational Marginal Pricing (LMP)data. LMP data is considered to be vital for power traders engaged inactive trading, as well as for developing trading strategies, acrossvarious ISO/RTO markets. Accordingly, correlation between LMP data,power data, and the causal factors affecting at least this data from arecent or historical data analysis perspective would provide powertraders valuable insight into the market. From a historical dataperspective, the correlations between power data and LMP data in thepower trading markets, according to causal factors, would assist thepower trader in determining how the market would react in similarsituations in the future because power trading markets tend to mimictheir past/historical performance(s) when the same/similar causalfactors are presented.

However, current data analysis systems that are tailored towards powertraders for use in the power or energy trading markets only offer verylimited data analysis capabilities. First, current data analysis systemsdo not make use of the full set of available power and price data.Moreover, these systems operate in a static manner and do not supportdynamic data gathering, management, and analysis methodologies. Second,current data analysis systems also do not have the capability to gather,manage, and analyze data such that certain cause and effect scenarioscan be determined accurately. For example, using current systems, powerdata such as power pricing data from the various markets cannot bemanaged and analyzed, in view of usage, congestion, weather-relatedconditions, and transmission outages, such that cause and effect factorsare properly linked and identified to inform power traders to makeappropriate market decisions.

SUMMARY OF THE INVENTION

Accordingly, the invention encompasses systems and methods for gatheringand performing analyses on power data from multiple remote sources thatsubstantially obviates one or more problems due to limitations anddisadvantages of the related art.

An encompassed feature of the invention is a powerful, efficient, androbust power data management and analysis capabilities to allow powertraders to make well-informed, confident trades, as well as to developsimilar trading strategies.

Another encompassed feature of the invention is an efficient power datamanagement solution that seamlessly retrieves, formats, and analyzeslarge quantities of power data from many remote sources, and providesvarious reports.

Additional features and advantages of the invention will be set forth inthe description which follows, and in part will be apparent from thedescription, or may be learned by practice of the invention. Theembodiments and other advantages of the invention will be realized andattained by the structure particularly pointed out in the writtendescription and claims hereof as well as the appended drawings.

To achieve these and other advantages and in accordance with the purposeof the invention, as embodied and broadly described, the systems andmethods for gathering and performing complex analyses on power data frommultiple remote sources includes a system, including a data gatheringunit to gather power data and locational marginal pricing (LMP) datafrom a plurality of remote power data sources and to convert the powerdata and the LMP data into a common data format; a data analysis unit tocorrelate the power data with the LMP data based on causal factors; adatabase to store at least the gathered power data and the LMP data, theconverted power data and the LMP data, and the correlated data of causalfactors; and a display unit to display at least one of the convertedpower data and the LMP data, and the correlated data of causal factors.

Another illustrative embodiment of the invention encompasses methodsincluding the steps of: gathering power data and LMP data from aplurality of remote power data sources and converting the power data andthe LMP data into a common data format; correlating the power data withthe LMP data based on causal factors; storing at least the gatheredpower data and the LMP data, the converted power data and the LMP data,and the correlated data of causal factors; and displaying at least oneof the converted power data and the LMP data, and the correlated data ofcausal factors.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory and areintended to provide further explanation of the invention as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are included to provide a furtherunderstanding of the illustrative embodiments of the invention and areincorporated in and constitute a part of this specification, illustrateembodiments of the invention and together with the description serve toexplain the principles of the invention.

FIG. 1 is a block diagram illustrating an exemplary embodiment of thesystem and data architecture of the present invention;

FIG. 2 illustrates an exemplary embodiment of the price reporting unitover a power user access interface;

FIGS. 2(a)-(o) illustrate various exemplary embodiments of the pricereport types over a power user access interface.

FIG. 3 illustrates an exemplary embodiment of the DA, RT, and loaddetails power node report interface;

FIG. 4 illustrates an exemplary embodiment of the DA, RT, and loadaverages power node report interface;

FIG. 5 illustrates an exemplary embodiment of the DA, RT, and loadstatistics power node report interface;

FIG. 6 illustrates an exemplary embodiment of the LMP frequencydistribution power node report interface;

FIG. 7 illustrates an exemplary embodiment of the DA and RT correlationpower node report interface;

FIG. 8 illustrates an exemplary embodiment of the ISO footprint reportinterface;

FIG. 9 illustrates an exemplary embodiment of the report preferencesunit interface;

FIG. 10 illustrates an exemplary embodiment of the chart analysis powernode report interface;

FIG. 11 illustrates an exemplary embodiment of the price look-backreport interface;

FIG. 12 illustrates an exemplary embodiment of a five-minute tickerreport interface; and

FIG. 13 illustrates an exemplary embodiment of a daily market summaryreport interface.

DETAILED DESCRIPTION OF THE INVENTION

The invention encompasses a system including:

a data gathering unit to gather power data and locational marginalpricing (LMP) data from a plurality of remote power data sources and toconvert the power data and the LMP data into a common data format;

a data analysis unit to correlate the power data with the LMP data foridentifying causal factors;

a database to store at least the gathered power data and the LMP data,the converted power data and the LMP data, and the correlated data ofcausal factors; and

a display unit to display at least one of the converted power data andthe LMP data, and the correlated data of causal factors.

In certain illustrative embodiments, the data gathering unit gatherspower data and LMP data over a network.

In certain illustrative embodiments, the system further comprises anaccess unit to grant access to one or more remote users.

In certain illustrative embodiments, the one or more remote users arepower traders in at least one power trading market.

In certain illustrative embodiments, the system further comprises aremote user device.

In certain illustrative embodiments, the display unit transmits displaysignals to the remote user device.

In certain illustrative embodiments, the system further comprises aprice reporting unit to generate at least one price summary report.

In certain illustrative embodiments, the price summary report ispresented based on filter criteria, the filter criteria including atleast one of a price report type, start date, end date, day type, hourtype, independent system operator (ISO) type, node type, delta value,standard deviation value, rank-by value, and LMP type.

In certain illustrative embodiments, the system further comprises achart analysis unit to compare at least one of the converted power dataand the LMP data and the correlated data of causal factors and togenerate at least one chart analysis report.

In certain illustrative embodiments, the chart analysis report isgenerated based on filter criteria, the filter criteria including atleast one of a start date, an end date, a day type, a hour type, a timezone type, a plurality of node types, a plurality of ISO/zone types, aplurality of weather types, and a compare nodes type.

In certain illustrative embodiments, the system further comprises aprice look-back unit to retrieve the converted power data and the LMPdata, and the correlated data of causal factors according to at leastlook-back criteria.

In certain illustrative embodiments, the look-back criteria includes atleast one of a temperature value, a load value, an outage value, an ISOtype, a weather type, a forecast date, a day value, a hour type, amatching hour value, a start date, and an end date.

In certain illustrative embodiments, the look-back criteria includes atleast one of a fuel index type, a fuel index price, a constraint type,and a transmission outage type.

In certain illustrative embodiments, the retrieved data is passed to aprice reporting unit to generate at least one price look-back reportaccording to filter criteria.

In certain illustrative embodiments, the filter criteria includes atleast a price report type.

In certain illustrative embodiments, the system further comprises alook-back results unit to display the retrieved data according to atleast one of a date value, a day value, an average load forecast value,an outage value, an actual outage value, an average temperature value, aspot fuel value, and a price report type.

In certain illustrative embodiments, the system further comprises acompare node unit to compare nodes to the retrieved data.

In certain illustrative embodiments, the price report type includes atleast one of a scouting summary, scouting detail, hourly spread, hourlyaverages, node ranking, top nodes, LMP breakdown, day ahead (DA)constraints, constraint frequency, weather forecast, weather forecastversus actual forecast, fuel prices, financial transmission rights (FTR)monthly auction, RSG/OP reserves, and transmission outages.

In another embodiment, the invention encompasses a computer-implementedmethod, including the steps of:

gathering power data and locational marginal pricing (LMP) data from aplurality of remote power data sources and converting the power data andthe LMP data into a common data format;

correlating the power data with the LMP data for identifying causalfactors;

storing at least the gathered power data and the LMP data, the convertedpower data and the LMP data, and the correlated data of causal factors;and

displaying at least one of the converted power data and the LMP data,and the correlated data of causal factors.

In certain illustrative embodiments, the gathering of power data and LMPdata from a plurality of remote power data sources is performed over anetwork.

In certain illustrative embodiments, the computer-implemented furthercomprises the step of granting access to one or more remote users.

In certain illustrative embodiments, the one or more remote users arepower traders in at least one power trading market.

In certain illustrative embodiments, the displaying step furthercomprises displaying signals on a remote user device.

In certain illustrative embodiments, the displaying signals aretransmitted to the remote user device.

In certain illustrative embodiments, the computer-implemented methodfurther comprises the step of generating at least one price summaryreport.

In certain illustrative embodiments, the price summary report isgenerated based on filter criteria, the filter criteria including atleast one of a price report type, start date, end date, day type, hourtype, independent system operator (ISO) type, node type, delta value,standard deviation value, rank-by value, and LMP type.

In certain illustrative embodiments, the computer-implemented methodfurther comprises the step of comparing at least one of the convertedpower data and the LMP data and the correlated data of causal factorsand generating at least one chart analysis report.

In certain illustrative embodiments, the chart analysis report isgenerated based on filter criteria, the filter criteria including atleast one of a start date, an end date, a day type, a hour type, a timezone type, a plurality of node types, a plurality of ISO/zone types, aplurality of weather types, and a compare nodes type.

In certain illustrative embodiments, the computer-implemented methodfurther comprises the step of retrieving the converted power data andthe LMP data, and the correlated data of causal factors according to atleast look-back criteria.

In certain illustrative embodiments, the look-back criteria includes atleast one of a temperature value, a load value, an outage value, an ISOtype, a weather type, a forecast date, a day value, a hour type, amatching hour value, a start date, and an end date.

In certain illustrative embodiments, the look-back criteria includes atleast one of a fuel index type, a fuel index price, a constraint type,and a transmission outage type.

In certain illustrative embodiments, the computer-implemented methodfurther comprises the step of passing the retrieved data and generatingat least one price summary report according to filter criteria.

In certain illustrative embodiments, the filter criteria includes atleast a price report type.

In certain illustrative embodiments, the computer-implemented methodfurther comprises the step of displaying the retrieved data according toat least one of a date value, a day value, an average load forecastvalue, an outage value, an actual outage value, an average temperaturevalue, a spot fuel value, and a price report type.

In certain illustrative embodiments, the computer-implemented methodfurther comprises the step of comparing nodes to the retrieved data.

In certain illustrative embodiments, the price report type includes atleast one of a scouting summary, scouting detail, hourly spread, hourlyaverages, node ranking, top nodes, LMP breakdown, day ahead (DA)constraints, constraint frequency, weather forecast, weather forecastversus actual forecast, fuel prices, financial transmission rights (FTR)monthly auction, RSG/OP reserves, and transmission outages.

In another embodiment, the invention encompasses a computer-readablestorage medium, storing one or more programs configured for execution byone or more processors, the one or more programs comprising instructionsto:

gather power data and locational marginal pricing (LMP) data from aplurality of remote power data sources and convert the power data andthe LMP data into a common data format;

correlate the power data with the LMP data for identifying causalfactors;

store at least the gathered power data and the LMP data, the convertedpower data and the LMP data, and the correlated data of causal factors;and

display at least one of the converted power data and the LMP data, andthe correlated data of causal factors.

Reference will now be made in detail to the embodiments of theinvention, examples of which are illustrated in the accompanyingdrawings. Wherever possible, like reference numbers will be used forlike elements.

FIG. 1 is a block diagram illustrating an exemplary embodiment of thesystem and data flow architecture of the present invention. Asillustrated in FIG. 1, the exemplary embodiment includes data processinglayers and communication interface layers. The data processing layersmay, for example, include a remote power data sources layer 20, a systemlayer 30 (comprising at least one or more of a computer server, adatabase, and a network communication device) and a power trading marketlayer 40. Further, the communication interface layer may include localarea networks (LAN) or wide area networks (WAN) 50 and 60. However,other data and communication layers may be used without departing fromthe scope of the invention.

As further illustrated in FIG. 1, the remote power data sources layer 20includes several power and price data stores 21-25. Each of the datastores 21-25 contains power and price data from a specific IndependentSystem Operator (ISO) or Regional Transmission Organization (RTO) powermarket. For example, the MISO 21 data store contains power and pricedata from the Midwest ISO; the PJM 22 data store contains power andprice data from the Pennsylvania, New Jersey, and Maryland ISO; theNEISO 23 data store contains power and price data from the New EnglandISO; the NYISO 24 data store contains power and price data from the NewYork ISO; and, the RTO 25 data store contains power and price data froman RTO. Each of these data stores 21-25 is publicly accessible and maybe queried directly, or via a local or wide area network 50 and/or 60.

Because the data formats (also referred to as data schemas) of the datastores 21-25 may not be consistent among the data stores 21-25, the datagathering unit 31 implements a plurality of customized routines toconvert the power and price data into at least one common data format.To accurately complete the data formatting routine, the data gatheringunit 31 is regularly updated with the latest customized routines thatinclude data format changes on each data store. Accordingly, the datagathering unit 31 normalizes data from the disparate remote data sourcesfor efficient data handling by other units within the system layer 30.Additionally data gathering unit 31 acts as a layer of abstraction thatinsulates/encapsulates the other units from having to undergo changes asdata stores may edit their respective data formats. The data gatheringunit 31 may also implement the actual data format changes that should bemade as data stores edit their respective data formats. Alternatively,the data gathering unit 31 may invoke outside procedures, which areupdated as any edits are made to the data formats. Further, the outsideprocedures may be executed via the master data store 32, or any otherdata store that supports the procedures' successful execution. The datagathering unit 31 may, in some instances, execute on an hourly basisbecause the ISO/RTO markets publish and/or update their data at suchtime intervals. In a one day period the ISO/RTO markets could publishand/or update their data—in different data formats—at least twenty-four(24) times; this frequency factor when multiplied by the number ofISO/RTO markets, five (5) of which are shown in the exemplary embodimentof FIG. 1, is an example of the operational frequency of the datagathering unit 31. For example, according to this sample frequencyscenario as applied to the exemplary embodiment, the data gathering unit31 would execute one hundred-twenty (120) different times in just a oneday period. If either the frequency of the data's publications/updatesis increased (i.e., bi-hourly or even more frequent), or the number ofaccessed ISO/RTO markets is increased, the data gathering unit 31 wouldhave to execute at frequency intervals paralleling those of the ISO/RTOmarkets' publications/updates.

After the data gathering unit 31 gathers and normalizes the data fromthe data stores 21-25, the gathered power and price data converted to acommon data format are sent from the data gathering unit 31 to themaster data store 32 for storage and later retrieval/query access byother units of the system layer 30. The master data store 32 may beimplemented using any type of data base management system (DBMS) suchas, for example, SQL Server™, Oracle™, or Access™. However, other DBMSor data storage solutions (e.g., files, memory, etc.) may be usedwithout departing from the scope of the invention.

The master data store 32 includes power data, price data (includinglocational marginal pricing (LMP) data), and causal factors dataobtained from the various remote power data sources 20. In an exemplaryembodiment, the gathered power data and price data from each ISO/RTOregion may be logically/physically stored together, while the same datamay be stored separately from another ISO/RTO region's data.Furthermore, with each of the various ISO/RTO databases, the power andprice data is partitioned according to a month of the year to which itapplies. As a consequence of this multi-tiered data organization andstorage scheme, the queries applied against each tier of data may onlytraverse a specific ISO/RTO region's data according to a specific monthof the year, and not necessarily the entire depth and breadth of ISO/RTOregion data that is available on the master data store 32. Of course,one of ordinary skill in the art will recognize that queries (like thosewritten using the structure query language, SQL) can be broadened ornarrowed accordingly and do not have to conform to this specificimplementation. For example, SQL or other types of queries may bewritten in such a way as to combine months or ISO/RTO regions/markets inorder to broaden the scope of their data coverage. Similarly, thequeries may also be limited to narrower subsets of data within a month,regional power markets within an ISO/RTO region/market, and/or even tospecific power nodes within regional power markets; these queries mayalso be further executed alone or in combination with other queryelements for access to an even narrower set of data, as long as thoseother elements are defined and available in the database schema.

As depicted in the exemplary embodiment of FIG. 1, the access unit 33and the data analysis unit 34 interface with the power and price datastores 21-25 or, more generally, the remote power data sources 20. Asopposed to maintaining independent and/or separate connections to eachone of the remote power data sources 20, which may also be availablelocally rather than remotely, and switching between each connectionaccording to whichever data store 21-25 is actively being queried(whether serially or in parallel), the exemplary embodiment maintainsthe master data store 32 in a manner that eliminates this problem. Themaster data store 32 may contain stored procedures that are customizedfor execution against each power and price data store 21-25 (each ofwhich might have different data formats). As a result, any subsequentunit within the system 30 may query the master data store 32, regardlessof the power and price data store 21-25 from which information issought. The master data store's 32 stored procedures may be executedeither directly from the master data store 32 against each of the remotepower data sources 20, or via the data gathering unit 31; another layerof data abstraction may be added such that the stored procedures areexecuted neither through the master data store 32 nor the data gatheringunit 31, but rather via another unit or DBMS. The master data store 32of the system 30 may serve as the physical and/or logical single accesspoint to the one or more power and price data stores 21-25. Thus, theaccess unit 33 and the data analysis unit 34 may maintain one connectionto the master data store 32, without the need for maintaining multipleand/or separate connections to, generally, the remote power data sources20 or, specifically, each of the power and price data stores 21-25.

As further illustrated in the exemplary embodiment of FIG. 1, the dataanalysis unit 34 maintains several data flow interfaces/connection withseveral other units/data store(s). The data analysis unit 34 maintains adata flow interface/connection with the master data store 32, the accessunit 33, and the display unit 35. The data gathering unit 32, althoughnot shown in the exemplary embodiment of FIG. 1, may also maintain adata flow interface/connection with remote power data sources 20, and/oranother data store, in parallel or separate from the master data store32.

The data analysis unit 34 performs analyses on power data and price datato correlate them individually or in the aggregate with one or morecausal factors. It is these analyses and other similar ones that areprocessed by the data analysis unit 34, verified against a power traderuser's 43 access permission(s), and may then be sent via either theaccess unit 33 or display unit 35 to the power trader access unit 41.The transmission of the results of these analyses, and similar ones, maybe performed directly, or over a LAN or WAN 60 (whether wired orwireless). Once transmitted to the one or more power trader access units41 and to the power trader user(s) 43 in one or more power tradingmarkets 40, the results may then be displayed over a processing/displaydevice 42 like a laptop, PDA, mobile telephone, or other similarprocessing/display device, capable of running a power trader access unit41 and/or capable of receiving the results from the access unit 33 ordisplay unit 35. A power trader access unit 41 may be an internetbrowser like those offered by Microsoft, Netscape, or Mozilla (e.g.,Internet Explorer™, Navigator™, Firefox™), a standalone application, ormay be a port capable of sending and receiving data, whether that portbe a direct-connection type port, or a network-connection type port. Inaccordance with the exemplary embodiment of FIG. 1, the access unit 33first receives signals from the one or more power trader access units 41and determines whether the power trader 43 may or may not be providedaccess to the features of the system 30. Whether the power trader 43 orremote user is granted access or not, the power trader 43 or remote userreceives the appropriate access signals sent from the access unit 33 andreceived by the power trader access unit 41. Similarly, the display unit35 also transmits the appropriate display signals, often through theaccess unit 33 and then to the power trading access unit 41, accordingto whether the power trader is granted access or not. The power trader43 or remote user subsequently learns of a permission status via thespecific processing/display device 42 or other remote user device thatmight be used. The specific implementation and connection of the powertrader access unit 41, with the units in the system 30, will beunderstood by one of ordinary skill in the art as not limiting the scopeof the features embodied by the unit, or those it may interface with.

Further, as illustrated in the exemplary embodiment of FIG. 1, the dataflow interface/connection that the data analysis unit 34 maintains withthe master data store 32 acts as a source data stream from which thecommonly formatted power and price data, which results from theoperation(s) of the data gathering unit 31, is received. Once received,the commonly formatted power and price data (including LMP data), isanalyzed according to data processing algorithms that correlate thecommonly formatted power and price data with LMP data for identifyingcausal factors. These causal factors include, but are not limited to,the following type(s) of power data: power usage, congestion,weather-related conditions (e.g., temperature, dew point, and/orrelative humidity), transmission outages, peak power data, off-peakpower data, binding constraints, fuel price(s), and/or time zone. Thecorrelation operation(s) that are performed by the algorithms may beperformed on one or more ISO/RTO regions/markets, which may furtherinclude many regions and, for example, hundreds or thousands of powernodes, or the correlation may be performed on a narrower set of datalike that of one or a couple of nodes; either range of processing isfully supported by the data analysis unit 34 and its algorithms. Throughthe execution of the data analysis unit 34 and its algorithmicallydetermined correlations, power traders 43 in one or more power tradingmarkets 40 are able to execute power trades, as well as develop similarpower trading strategies, with the important advantage of realizing thecausal factors or cause-and-effect scenarios that correspond to specificconditions in ISO/RTO regions/markets, and/or power nodes. Moreover, asdescribed below in more detail and illustrated in the exemplaryembodiments of FIGS. 2-11, through the execution of the data analysisunit 34 and its algorithmically determined correlations, power traders43 in one or more power trading markets 40 are further presented with avariety of options/tools for selecting the various ways to customizetheir queries and to receive the corresponding results through severalkinds of reports. Thus, the system 30 operating as a whole offers a verydynamic, customized, power trader-friendly environment, whileconcurrently managing uncommonly formatted data from the remote powerdata sources 20, and executing algorithms to aid the user in identifyingcause-and-effect scenarios.

Power traders 43 in one or more power trading markets 40 are capable ofaccessing several features through the power trader access unit 41 as itinterfaces with the access unit 33 and/or the display unit 35. Thefeatures are specifically aimed at customizing queries against themaster data store 32, selecting one of several types of features forcustomizing the queries, and receiving several kinds of reports fordisplay via the power trader access unit 41 and a processing/displaydevice 42.

The features for customizing queries and the kinds of reports areembodied in FIGS. 2-11. Moreover, the features may be categorized intothe following types: price reporting, chart analysis, and price-lookback. First, the price reporting feature, which is implemented by theprice reporting unit, generates one or more price summary reportsaccording to user-selected filter criteria. Second, the chart analysisfeature, which is implemented by the chart analysis unit, compares oneor more of the common format power data and price data (including LMPdata), as well as the correlated data, including causal factor(s) data,according to user-selected filter criteria. Third, the price look-backfeature, which is implemented by a price look-back unit, retrieves andanalyzes certain of the common format power data and price data,including LMP data, as well as the correlated data (including causalfactor(s) data), according to user-selected look-back criteria, andgenerates one or more price look-back reports.

FIG. 2 is an exemplary embodiment of a price summary report 100 of theprice reporting feature, over a power user access interface 105. Asillustrated in FIG. 2, the price summary report 100 is comprised of aplurality of filter criteria that may be selected by a power trader user43. The filter criteria permit a power trader user 43 to customize theprice summary report 100 such that the user can view the most relevantcause-and-effect scenarios for the user's trading strategies. The filtercriteria include, but are not limited to, the following: a price reporttype 110, a start date 111, an end date 112, a day type 113, a hour type114, an independent system operator (ISO) type 115, a node type 116, adelta value 117, a standard deviation value 118, a rank-by value 119,and an LMP type 120. One or many of the filter criteria may be chosen.Further, the price report type 110 includes, but is not limited to, thefollowing report types: scouting summary 125, scouting detail 130,hourly spread 135, hourly averages 140, node ranking 145, top nodes 150,LMP breakdown 155, day ahead (DA) constraints 160, constraint frequency165, weather forecast 170, weather forecast versus actual forecast 175,fuel prices 180, financial transmission rights (FTR) monthly auction185, RSG/OP reserves 190, and transmission outages 195. Once selected bythe power trader user 43, and either an executable button like thesearch button 121 is invoked or automatic execution occurs, the filtercriteria is/are sent to the price reporting unit for processing by theappropriate data analysis unit 34 algorithm(s). Subsequent to thealgorithm's(s') processing of the appropriate commonly formatted powerand price data with LMP data, including causal factors, and the selectedfilter criteria, the resulting data is sent from the price reportingunit to either the access unit 33 and/or display unit 35 for subsequenttransmission to the power trader access unit 41 and a processing/displaydevice 42. Then, at that time or at a later point in time the powertrader 43 may read and analyze the displayed resulting data in order todetermine and/or analyze prospective power trades and power tradingstrategies according to the resulting data. In addition, the resultingdata is displayed to the power trader 43 based on the selected pricereport type 110, each of which may display a different set and/orsubset(s) of the resulting data. Each power trader 43, based on userpreference(s), may freely choose the price report type 110 that bestsuits preferred power trading needs and strategies. Moreover, the sameprice report may show anything, for example, from a price for a givenhour averaged over a specific timeframe, to all of the prices averagedover the same timeframe.

An exemplary embodiment of each of the price report types, listed above,is illustrated in FIGS. 2(a)-(o), respectively. Many of the price reporttypes (like, for example, scouting summary 125, as shown in FIG. 2(a))also contain a “drill-down” feature 122 that may, for example, providehourly detail power and price data about a specific power node.

FIG. 3 illustrates an exemplary embodiment of the DA, RT, and loaddetails power node report 200, over a power user access interface 105.This report is the first of several types of reports, which areillustrated in FIGS. 4-7 (described below), that allow power traderusers 43 to quickly navigate from the more general price report types(described above) to these specific reports with the capability to beable to analyze finite hourly level detail(s). For example, thesereports are capable of displaying data that may be analyzed (in at leastgraph or tabular format) based on a range as specific as five (5) minuteintervals for past and current hours. The report illustrated in FIG. 3is capable of displaying, for example, hourly level data for a specific,user selected date interval, day(s), and hour(s). Hourly price data isdisplayed by default both graphically and in tabular format. The userhas the capability to change the presented data by selecting from thenumber of options 205 available like: DA, RT, and Delta. These options205 are further displayed according to, for example: price, loss,congestion, standard deviation, 15 DMA, 30 DMA, 45 DMA, RSG, ISO Load,ISO Outage, weather locale, temperature, dew point, and relativehumidity. Once the data is presented at least based on these default orcustom capabilities, for example, via the power trader access unit 41and a processing/display device 42, the power trader user 43 may viewthe data to determine cause-and-effect scenarios.

FIG. 4 illustrates an exemplary embodiment of the DA, RT, and loadaverages power node report 300, over a power user access interface 105.This report shares many of the characteristics that define the detailspower node report 200. In addition, the report has the capability topresent, for example, hourly level data that is averaged for each hourof the day (i.e., he1, he2, . . . he24) over a user selected timeinterval.

FIG. 5 illustrates an exemplary embodiment of the DA, RT, and loadstatistics power node report 400, over a power user access interface105. This report also shares many of the characteristics that define thedetails power node report 200. In addition, however, the report has thecapability to present, for example, key price statistics 405 over thepast one or more years, and/or over a user selected time interval. Thereport is capable of presenting, for example, the following key pricestatistics 405: minimum price, maximum price, 15-day average, 30-dayaverage, and 45-day average.

FIG. 6 illustrates an exemplary embodiment of the LMP frequencydistribution power node report 500, over a power user access interface105. This report also shares many of the characteristics that define thedetails power node report 200. In addition, however, the report has thecapability to present, for example, the percentage of time that LMP datameets specific price ranges.

FIG. 7 illustrates an exemplary embodiment of the DA and RT correlationpower node report 600, over a power user access interface 105. Thisreport also shares many of the characteristics that define the detailspower node report 200. In addition, however, the report has thecapability to present, for example, a user-selected comparison of aspecific power node against all other power nodes, or a specific subsetof power nodes, within an ISO/RTO region/market. Moreover, the user ispresented with at least two options, one for computing an r-valuecorrelation coefficient 605, and another for comparing an LMP price 610on an hour-by-hour basis. The computation of an r-value correlationcoefficient uses a standard r-value calculation and may be performed bythe data analysis unit 34, a stored procedure of the master data store32, or another unit or data store of the system 30. The r-valuecorrelation coefficient calculations may be used by power trader users43 to identify power nodes with similar trends, as well as potentialhedging opportunities. As for the other option, comparing an LMP price,the comparison is performed between a price for each hour to every otherpower node, or a specific subset of power nodes, within an ISO/RTOregion/market; it may also compute the average difference between them.

FIG. 8 illustrates an exemplary embodiment of the ISO footprint report700, over a power user access interface 105. This report also sharesmany of the characteristics that define the details power node report200. Specifically, this report provides the capability to analyze aspecific ISO/RTO region's/market's load data as it compares to outagedata 705.

FIG. 9 illustrates an exemplary embodiment of the report preferencesunit 800, over a power user access interface 105. Specifically, thisreport provides the capability to set specific report preferences like,for example: from-to date range 805, power node type 810, ISO/RTO type815, day type 820, and hour(s) type 825. The preferences may be saved bythe user and/or loaded from an earlier time when they were saved. Inaddition, once preferences are saved and/or loaded they may besubsequently reflected in the reports.

FIG. 10 illustrates an exemplary embodiment of a chart analysis powernode report 900 of the chart analysis feature, over a power user accessinterface 105. As illustrated in FIG. 10, the chart analysis report iscomprised of a plurality of filter criteria 905 that may be selected bya power trader user 43. The filter criteria 905 permit a power traderuser 43 to customize the chart analysis power node report 900 such thatuser can compare nodes and view the most relevant cause-and-effectscenarios for the trading strategies. The filter criteria generallyinclude common format power data and price data (including LMP data), aswell as the correlated data including causal factor(s). Specifically,the filter criteria include, but are not limited to, the following: astart date 910, an end date 915, a day type 920, a hour type 925, a timezone type 930, a plurality of node types 935, a plurality of ISO/zonetypes 940, a plurality of weather types 945, and a compare nodes type950. The exemplary embodiment illustrated in FIG. 10 shows that up tofive (5) power nodes may be compared simultaneously. In otherembodiments, however, the user may be able to select more or less powernodes. Furthermore, users may also choose to include load and outagedata, as well as weather conditions data. Again, the exemplaryembodiment illustrated in FIG. 10 illustrates that data from up to two(2) ISO/RTO regions/markets could be selected, as well as weatherconditions data from up to two (2) cities. In other embodiments,however, the user may be able to select more or less ISO/RTOregions/markets, as well as weather conditions data from more or lesscities.

FIG. 11 illustrates an exemplary embodiment of a price look-back report1000 of the price look-back feature, over a power user access interface105. The price look-back feature permits a power trader user 43 toinvoke the retrieval and analysis of certain of the common format powerdata and price data (including LMP data), as well as the correlated dataincluding causal factor(s), according to user-selected look-backcriteria, and to generate one or more price look-back reports 1000. Asillustrated in FIG. 11, the look-back criteria 1005 include, but are notlimited to, for example: fuel index type 1010, fuel index price 1015,constraint type 1020, and transmission outage type 1025. Further, asalso illustrated in FIG. 11, the look-back criteria 1005 include, butare not limited to, for example: temperature value 1030, load value1035, outage value 1040, ISO type 1040, weather type 1050, forecast date1055, day value 1060, hour type 1065, matching hour value 1070, startdate 1075, and end date 1080. Specific tolerance ranges may be selectedfor one or more of the look-back criteria. Once the user has selectedthe look-back criteria, and the data is retrieved and analyzed by theprice look-back feature, it is passed on to a look-back results unit ordisplayed directly. The retrieved and analyzed data is that whichcorresponds to the specific criteria selected; the data may be forcorresponding days, other time frames, or based on one or more othermetrics. The look-back results unit implements a look-back resultsfeature that displays the retrieved and analyzed data according to, forexample, one or more of the following criteria: date value 1085, dayvalue 1090, average load forecast value 1095, outage value 2000, actualoutage value 2005, average temperature value 2010, spot fuel value 2015,and price report type 2020. At this point in time, the user may invokethe price reporting feature on certain of the retrieved and analyzeddata displayed by the look-back results feature. The user may alsoretrieve LMP data for specific power nodes for the retrieved andanalyzed data. If the user invokes the price reporting feature, the usermay then also choose one or more of the price report types 110(discussed above) and generate one or more price summary reports,accordingly. In addition to, or instead of, invoking the price reportingfeature on certain of the retrieved and analyzed data displayed by thelook-back results feature, the user may also choose to invoke thecompare node feature, which is implemented by the compare node unit, inorder to compare one or more specific power nodes to the retrieved anddisplayed data.

The features and capabilities of the price look-back feature, as well assome of the other features of the exemplary embodiment, provide powertrader users 43 with powerful tools to at least retrieve and analyze themost proper and accurate historical power and price data, which serve asa reliable indicator for today's power prices and market performance. Inaddition, power trader users 43 are also able to at least analyzeaccurate cause-and-effect scenarios that permit the traders to makeconfident, informed trades, as well as to develop successful powermarket trading strategies.

FIG. 12 illustrates an exemplary embodiment of a five-minute tickerreport 1100 interface, over a power user access interface 105. Asillustrated in FIG. 12, the five-minute ticker report may comprise pricenode summary charts 1105, 1110. The five-minute ticker report is auser-customizable report that provides power trader users 43 with theability to identify a list of nodes 1135 for which they would like totrack data such as average RT price 1140 and DA price 1145, accordingto, for example, five-minute intervals (e.g., every 5 minutes, 10minutes, 15 minutes, and so on). Other data may also be reported 1110such as, for example, constraints 1140 affecting DA pricing of one ormore ISOs 1115, as well as the start time 1125 and end time 1130 of suchconstraints 1140. The five-minute report may be implemented by the pricereporting unit of the price reporting feature.

FIG. 13 illustrates an exemplary embodiment of a daily market summaryreport 1200 interface. As illustrated in FIG. 13, the daily marketsummary report 1205 may comprise an ISO-level summary 1210 and a nodedetails-level summary 1215. The daily market summary report 1205 may betransmitted daily to power trader users 43 via, for example, email orother communication means. The report 1205 may be transmitted at anytime after the next day's DA prices are available from one or moreISOs/RTOs. The report 1205 may provide power trader users 43 with asummary or snapshot of one or more corresponding power markets. Forexample, the report 120 may provide DA and RT prices for average peaktimes 1220 and average off-peak times 1225. The report 1205 may beimplemented by the price reporting unit of the price reporting feature.

The exemplary embodiments described herein not only manage, track, andanalyze power and pricing data, but the analysis capabilities aid powertraders in determining the causal factors that drive specific ISO/RTOpower trading markets. In fact, a system and method are provided for,among other things, evaluating supply and demand fundamentals, powerdata, pricing data, causal factors, and determining a “real time” (RT)price.

It will be apparent to those skilled in the art that variousmodifications and variations can be made in the system and method forgathering and performing complex analyses on power data from multipleremote sources, of the present invention, without departing form thespirit or scope of the invention. Thus, it is intended that theinvention cover the modifications and variations of this inventionprovided they come within the scope of the appended claims and theirequivalents.

What is claimed is:
 1. A system, comprising: a processor; a displayunit; and a non-transitory data storage device that includesinstructions when executed by the processor comprising: a data gatheringunit to gather power data and locational marginal pricing (LMP) datafrom a plurality of remote power data sources, wherein the power dataincludes real-time power data; and a data analysis unit to correlate thereal-time power data with the LMP data for identifying causal factors.2. The system of claim 1, wherein the data gathering unit converts thepower data and the LMP data into a common data format.
 3. The system ofclaim 2, further comprising a database to store at least the gatheredreal-time power data and the LMP data, the converted power data and theLMP data, and the correlated data of causal factors.
 4. The system ofclaim 1, wherein the data gathering unit gathers the power data and LMPdata over a network.
 5. The system of claim 1, further comprising anaccess unit to grant access to one or more remote users.
 6. The systemof claim 5, wherein the one or more remote users are power traders in atleast one power trading market.
 7. The system of claim 1, furthercomprising a remote user device.
 8. The system of claim 1, wherein thedisplay unit transmits display signals to the remote user device.
 9. Thesystem of claim 1, further comprising a price reporting unit to generateat least one price summary report.
 10. The system of claim 9, whereinthe price summary report is presented based on filter criteria, thefilter criteria including at least one of a price report type, startdate, end date, day type, hour type, independent system operator (ISO)type, node type, delta value, standard deviation value, rank-by value,and LMP type.
 11. The system of claim 2, further comprising a chartanalysis unit to compare at least one of the converted power data andthe LMP data and the correlated data of causal factors and to generateat least one chart analysis report.
 12. The system of claim 11, whereinthe chart analysis report is generated based on filter criteria, thefilter criteria including at least one of a start date, an end date, aday type, a hour type, a time zone type, a plurality of node types, aplurality of ISO/zone types, a plurality of weather types, and a comparenodes type.
 13. The system of claim 2, further comprising a pricelook-back unit to retrieve the converted power data and the LMP data,and the correlated data of causal factors according to at leastlook-back criteria.
 14. The system of claim 13, wherein the look-backcriteria includes at least one of a temperature value, a load value, anoutage value, an ISO type, a weather type, a forecast date, a day value,a hour type, a matching hour value, a start date, and an end date. 15.The system of claim 13, wherein the look-back criteria includes at leastone of a fuel index type, a fuel index price, a constraint type, and atransmission outage type.
 16. The system of claim 13, wherein theretrieved data is passed to a price reporting unit to generate at leastone price look-back report according to filter criteria.
 17. The systemof claim 16, wherein the filter criteria includes at least a pricereport type.
 18. The system of claim 11, further comprising a look-backresults unit to display the retrieved data according to at least one ofa date value, a day value, an average load forecast value, an outagevalue, an actual outage value, an average temperature value, a spot fuelvalue, and a price report type.
 19. The system of claim 13, furthercomprising a compare node unit to compare nodes to the retrieved data.20. The system of claim 10, wherein the price report type includes atleast one of a scouting summary, scouting detail, hourly spread, hourlyaverages, node ranking, top nodes, LMP breakdown, day ahead (DA)constraints, constraint frequency, weather forecast, weather forecastversus actual forecast, fuel prices, financial transmission rights (FTR)monthly auction, RSG/OP reserves, and transmission outages.